CONOP—A QUANTITATIVE STRATIGRAPHIC SOFTWARE AND AN APPROACH TO ITS PARALLELIZATION
CONOP (Constrained Optimization) is a piece of software which improves the algorithm of graphic correlation. It can correlate all the sections from multi-dimensional space by the simulated annealing algorithm which can find the global or local optimal solution of this kind of problem. But as the amount of data increases, the elapsed time of calculation will rise amazingly. For a normal-size data set which contains 500 sections and 10000 events, it will take as long as several months to compute the data set in CONOP, which is unacceptable for an ordinary research task. Thereby, a significant improvement in the computation power of the CONOP software is necessary.
With the rapid development of computer hardware, multicore computers are becoming more and more popular. This kind of computers possess the capacity of parallel computing, therefore, to implement the parallelization of CONOP will be evidently practical that can greatly improve the processing efficiency of CONOP. We adopted C# to rewrite the core code of CONOP9 which was originally written by Fortran and parallelized the most time-consuming functions. As a result, the parallelized CONOP is averagely five times faster than the original Fortran version of CONOP while carried out the same big data set.